code: def test_fbeta_multiclass_with_micro_average(self, device: str):
      self.predictions = self.predictions.to(device)
      self.targets = self.targets.to(device)
  
      labels = [1, 3]
      fbeta = FBetaMeasure(average="micro", labels=labels)
      fbeta(self.predictions, self.targets)
      metric = fbeta.get_metric()
      precisions = metric["precision"]
      recalls = metric["recall"]
      fscores = metric["fscore"]
  
      # We keep the expected values in CPU because FBetaMeasure returns them in CPU.
      true_positives = torch.tensor([1, 1], dtype=torch.float32)
      false_positives = torch.tensor([3, 0], dtype=torch.float32)
      false_negatives = torch.tensor([0, 0], dtype=torch.float32)
      mean_true_positive = true_positives.mean()
      mean_false_positive = false_positives.mean()
      mean_false_negative = false_negatives.mean()
  
      micro_precision = mean_true_positive / (
          mean_true_positive + mean_false_positive
      )
      micro_recall = mean_true_positive / (
          mean_true_positive + mean_false_negative
      )
      micro_fscore = (2 * micro_precision * micro_recall) / (
          micro_precision + micro_recall
      )
      # check value
      assert_allclose(precisions, micro_precision)
      assert_allclose(recalls, micro_recall)
      assert_allclose(fscores, micro_fscore)
  
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from sklearn.metrics import precision_recall_fscore_support
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, run_distributed_test, global_distributed_metric
  from allennlp.training.metrics import FBetaMeasure
label_desc: Use image node_id 11 for calling the FBetaMeasureTest obj's underlying member method code with example usage: obj.test_fbeta_multiclass_with_micro_average(device) without return types